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Vector Databases & Embeddings

1 min readPublished 22 Jul 2026

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Embeddings and vector databases are the engine behind RAG and semantic search — they let computers find things by meaning, not just keywords.

Interactive explainer

Embeddings & Vector Search

Finding things by meaning, not keywords.

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Embeddings

Meaning as numbers.

A model turns text (or images) into a vector — a list of numbers capturing meaning. Similar meanings produce vectors that sit close together.

Check your understanding

1. What is an embedding?

2. What does semantic search match on?

Keep learning

Vector Databases Databases & SQL Microsoft AI-102

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The essentials

Embeddings — an AI model turns text (or images) into a list of numbers (a vector) that captures its meaning. Similar meanings sit close together in vector space.
Vector database — stores millions of these vectors and finds the nearest ones to a query extremely fast (approximate nearest-neighbour search).
Semantic search — searching by meaning: "how do I reset my password" matches a doc titled "account recovery" even without shared keywords.
Platforms — Pinecone, Weaviate, Chroma, and pgvector (which adds vectors to PostgreSQL).

This contrasts with traditional relational databases, which match exact values — a nice link across the Academy.

Interview Intelligence

How this topic actually shows up in interviews — and how to demonstrate you understand it.

Why employers ask about this

Vector search underpins modern AI applications, making it a valuable, current skill.

Technical questions

How does semantic search differ from keyword search?+

Semantic search matches by meaning using embeddings, so related concepts match even without shared keywords.

Behavioural questions

Describe learning a concept that connected two areas you knew.+

Show linking new AI ideas to existing database knowledge.

Real-world scenarios

“Keyword search misses relevant documents.”+

Expected answer: Use embeddings + a vector database for semantic search, matching by meaning rather than exact words.

Practise in AI Interview™Rehearse these questions, scored.Plan with Career Coach™A personalised path to your goal.Evidence it in Career Passport™Prove this skill to employers.

Employability Intelligence

Where this knowledge takes you — the jobs, skills and certifications it feeds into.

Relevant roles

AI EngineerData EngineerSearch Engineer

Skills you're proving

EmbeddingsVector databasesSemantic search

Recommended certifications

Microsoft AI-900 (AI Fundamentals)Microsoft AI-102AWS AI Practitioner

Career progression

AI & Automation Fundamentals → AI/data engineering roles.

What employers expect

That you can use AI tools effectively, understand their limits, and automate work responsibly.

Frequently asked questions

What is an embedding?

A numerical vector representation of text (or other data) that captures meaning, so similar items sit close together.

What is a vector database?

A database optimised to store embeddings and quickly find the most similar vectors to a query.

What is semantic search?

Searching by meaning rather than exact keywords, powered by embeddings and vector similarity.

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Missiora is an AI Employability Intelligence platform. Our resources are researched and reviewed by the Missiora team to help you measure, improve and prove your career readiness.